Problem
Research questions and friction points this paper is trying to address.
Developer Efficiency
Longitudinal Study
External Developers
Generative AI Barriers
Organizational Dependencies
Factors Impacting Developer Efficiency: Results from an Adaptive Longitudinal Study
🤖 AI Summary
This study addresses the challenge of capturing the dynamic evolution of developer productivity and emerging barriers by proposing ADEMM, an adaptive monitoring method. Through a longitudinal mixed-methods study involving 27 external developers, we employed iterative surveys and interviews to track productivity factors over time. The results reveal critical evolutionary patterns, identifying organizational dependencies as persistent bottlenecks and generative AI-related obstacles as late-emerging impediments, thereby validating ADEMM’s superiority over static assessment tools. These findings not only elucidate the dynamic mechanisms underlying productivity but also inform targeted organizational interventions. Ultimately, this work establishes a closed-loop framework that effectively translates theoretical insights into actionable practical improvements for enhancing developer efficiency in evolving software engineering environments.
📝 Abstract
Context: Developer efficiency is driven by technical, organizational, and personal factors, yet few longitudinal studies explore how these factors evolve over time. Objective: This study investigates the primary factors hindering the perceived efficiency of developers in a consulting and professional development context, analyzing how these factors vary across recurring data collection cycles and how they are described qualitatively. Method: We conducted a mixed-methods longitudinal case study applying the Adaptive Developer Efficiency Monitoring Method (ADEMM) to 27 external software developers, combining twelve waves of periodic surveys with eighteen semi-structured interviews, analyzed through statistical and thematic analysis. Results: The most frequent bottlenecks were organizational dependencies and waiting for external validation, which stayed structurally stable, followed by technical knowledge gaps, which declined as developers adapted. A generative AI usage barrier emerged qualitatively nine waves into the study, was incorporated into the survey instrument, and became the most frequently coded interview theme. Interviews corroborated the quantitative findings, with insufficient requirements documentation and organizational dependencies as the most recurrent themes alongside AI-related challenges. Conclusions: Perceived developer efficiency is highly dynamic and cannot be accurately captured through a single cross-sectional measurement. Adaptive monitoring via ADEMM identified an emerging factor, generative AI usage barriers, that a fixed instrument would have missed, and informed a concrete organizational intervention during the study. For organizations managing external developers, actions should target external dependencies, communication channels, and developers' evolving use of AI tools.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Adaptive Developer Efficiency Monitoring Method
Longitudinal Study
Generative AI Usage Barriers
Mixed-Methods
Dynamic Measurement
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